The Application of Natural Language Processing and tspDB to Predict Future Bitcoin Price

Abstract

Formerly, predictions of time series were usually based on numbers. In some cases, however, one has to make predictions based on linguistic information. In this paper, we experiment with time series forecasting based on textual information. We combine sentiment analysis with time series forecasting to predict fluctuations in variables that change over time. Due to the time-sensitivity of the target variable, models from the Time Series Prediction Database (tspDB) are used to infer the value of Bitcoin. We implemented an innovative architecture that accepts text as input and produces numerical predictions for certain values. The market value of bitcoin was used to verify the applicability of the architecture. The program inputs Twitter posts and outputs the market value of bitcoin for the next few days. The program first calculates a sentiment score (which reflects social media confidence in the price of bitcoin) and then makes a multivariate time series prediction of the price of bitcoin. The results can provide some insight into how the price of bitcoin fluctuates. The purpose of using Bitcoin as our primary benchmark is that we want to demonstrate a sound application of the combination of NLP and tspDB, and while Bitcoin price is not an objective variable, the method we apply can also be used in other situations that contain linguistic information as input.